Explore the vast range of titles available.

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

Purpose Drawing on the branded service encounters perspective, the purpose of this study is to investigate how frontline service employees’ environmentally irresponsible behaviors affect customers’ brand evaluations. Design/methodology/approach The research conducted two experiments. The first experiment explored the effect of frontline service employees’ environmentally irresponsible behaviors on customers’ brand evaluations via corporate hypocrisy. The second experiment explored the moderation effect of employees’ prototypicality and the importance of corporate social responsibility (CSR) among customers. Findings Experiment 1 indicates that for firms with a green brand image, frontline employees’ environmentally irresponsible behaviors result in customers’ perception that the firm is hypocritical, thus reducing their brand evaluations. Experiment 2 shows that employee prototypicality and CSR importance to the customer enhance the negative impact of frontline employees’ environmentally irresponsible behaviors on customers’ brand evaluations through customers’ perception of corporate hypocrisy. Research limitations/implications This study is one of the first efforts to explore how frontline service employees’ environmentally irresponsible behaviors affect customers’ responses. It helps understand the impact of frontline employees’ counter-productive sustainable behaviors on customers’ brand perception, as well as the relationship between CSR and employees. Practical implications This study suggests that firms’ green brand image does not always lead to positive customer response. When frontline employees’ behaviors are inconsistent with firms’ green brand image, it can trigger customers’ perceptions of corporate hypocrisy and thus influence their brand evaluations. Therefore, firms should train frontline service employees to make their behaviors align with the firms’ green brand image. Originality/value This study is one of the first efforts to explore how frontline service employees’ environmentally irresponsible behaviors affect customers’ responses. It helps understand the impact of frontline employees’ counter-productive sustainable behaviors on customers’ brand perception, as well as the relationship between CSR and employee.

Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which travels on the road network to the nearby task arc. The road network considered in previous research is undirected; however, in reality, the road network usually contains unidirectional arcs, i.e., the road network is asymmetric. Hence, we propose an asymmetric coordinated vehicle-drones arc routing mode for traffic patrolling. In this mode, a truck travelling on an asymmetric road network with multiple drones needs to patrol multiple task arcs, and the drones can be launched and recovered at certain nodes on the truck route, making it possible for drones and the truck to patrol the task in parallel. The total patrol time is the objective function that needs to be minimized given the time limit constraints of drones. The whole problem can be considered as an asymmetric arc routing problem of coordinating a truck and multiple drones. To solve this problem, a large-scale neighborhood search with simulated annealing algorithm (LNS-SA) is proposed. Finally, extensive computation experiments and a real case are carried out. The experimental results show the efficiency of the proposed algorithm. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.

Chinese named entity recognition (NER) is a fundamental natural language processing (NLP) task that involves identifying and categorizing entities in text. It plays a crucial role in applications such as information extraction, machine translation, and question-answering systems, enhancing the efficiency and accuracy of text processing and language understanding. However, existing methods for Chinese NER face challenges due to the disruption of character-level semantics in traditional data augmentation, leading to misaligned entity labels and reduced prediction accuracy. Moreover, the reliance on English-centric fine-grained annotated datasets and the simplistic concatenation of label semantic embeddings with original samples limits their effectiveness, particularly in addressing class imbalances in low-resource scenarios. To address these issues, we propose a novel Chinese NER model, LGDA, which leverages Label-Guided Data Augmentation to mitigate entity label misalignment and sample distribution imbalances. The LGDA model consists of three key components: a data augmentation module, a label semantic fusion module, and an optimized loss function. It operates in two stages: (1) the enhancement of data with a masked entity generation model and (2) the integration of label annotations to refine entity recognition. By employing twin encoders and a cross-attention mechanism, the model fuses sample and label semantics, while the optimized loss function adapts to class imbalances. Extensive experiments on two public datasets, OntoNotes 4.0 (Chinese) and MSRA, demonstrate the effectiveness of LGDA, achieving significant performance improvements over baseline models. Notably, the data augmentation module proves particularly effective in few-shot settings.

ObjectiveTo develop a gastric cancer (GC) risk prediction rule as an initial prescreening tool to identify individuals with a high risk prior to gastroscopy.DesignThis was a nationwide multicentre cross-sectional study. Individuals aged 40–80 years who went to hospitals for a GC screening gastroscopy were recruited. Serum pepsinogen (PG) I, PG II, gastrin-17 (G-17) and anti-Helicobacter pylori IgG antibody concentrations were tested prior to endoscopy. Eligible participants (n=14 929) were randomly assigned into the derivation and validation cohorts, with a ratio of 2:1. Risk factors for GC were identified by univariate and multivariate analyses and an optimal prediction rule was then settled.ResultsThe novel GC risk prediction rule comprised seven variables (age, sex, PG I/II ratio, G-17 level, H. pylori infection, pickled food and fried food), with scores ranging from 0 to 25. The observed prevalence rates of GC in the derivation cohort at low-risk (≤11), medium-risk (12–16) or high-risk (17–25) group were 1.2%, 4.4% and 12.3%, respectively (p<0.001).When gastroscopy was used for individuals with medium risk and high risk, 70.8% of total GC cases and 70.3% of early GC cases were detected. While endoscopy requirements could be reduced by 66.7% according to the low-risk proportion. The prediction rule owns a good discrimination, with an area under curve of 0.76, or calibration (p<0.001).ConclusionsThe developed and validated prediction rule showed good performance on identifying individuals at a higher risk in a Chinese high-risk population. Future studies are needed to validate its efficacy in a larger population.

Fact verification aims to verify the authenticity of a given claim based on the retrieved evidence from Wikipedia articles. Existing works mainly focus on enhancing the semantic representation of evidence, e.g., introducing the graph structure to model the evidence relation. However, previous methods can’t well distinguish semantic-similar claims and evidences with distinct authenticity labels. In addition, the performances of graph-based models are limited by the over-smoothing problem of graph neural networks. To this end, we propose a graph-based contrastive learning method for fact verification abbreviated as CosG, which introduces a contrastive label-supervised task to help the encoder learn the discriminative representations for different-label claim-evidence pairs, as well as an unsupervised graph-contrast task, to alleviate the unique node features loss in the graph propagation. We conduct experiments on FEVER, a large benchmark dataset for fact verification. Experimental results show the superiority of our proposal against comparable baselines, especially for the claims that need multiple-evidences to verify. In addition, CosG presents better model robustness on the low-resource scenario.

Document-level Event Argument Extraction (DEAE) aims to extract structural event knowledge composed of arguments and roles beyond the sentence level. Existing methods mainly focus on designing prompts and using Abstract Meaning Representation (AMR) graph structure as additional features to enrich event argument representation. However, two challenges still remain: (1) the long-range dependency between event trigger and event arguments and (2) the distracting context in the document towards an event that can mislead the argument classification. To address these issues, we propose a novel document-level event argument extraction model named AMR Parser and Sparse Representation (APSR). Specifically, APSR sets inter- and intra-sentential encoders to capture the contextual information in different scopes. Especially, in the intra-sentential encoder, APSR designs three types of sparse event argument attention mechanisms to extract the long-range dependency. Then, APSR constructs AMR semantic graphs, which capture the interactions among concepts well. Finally, APSR fuses the inter- and intra-sentential representations and predicts what role a candidate span plays. Experimental results on the RAMS and WikiEvents datasets demonstrate that APSR achieves a superior performance compared with competitive baselines in terms of F1 by 1.27% and 3.12%, respectively.

We study the price effects of changes to the S&P 500 index and document an asymmetric price response: There is a permanent increase in the price of added firms but no permanent decline for deleted firms. These results are at odds with extant explanations of the effects of index changes that imply a symmetric price response to additions and deletions. A possible explanation for asymmetric price effects arises from the changes in investor awareness. Results from our empirical tests support the thesis that changes in investor awareness contribute to the asymmetric price effects of S&P 500 index additions and deletions.

Adolescent Self-Injurious Behavior (SIB) is a significant global public health issue, with a lifetime prevalence rate of approximately 13.7%. As awareness of SIB rises, there is an urgent need for effective prediction mechanisms to enable early identification and intervention, reducing the risk of suicide and self-harm attempts. This study, grounded in Psychopathological Network Theory, uses machine learning and network analysis to explore the multidimensional structure of risk factors for adolescent SIB. A survey of 2047 adolescents aged 11 to 17 years in China analyzed 19 variables across physiological, psychological, and social domains. The Entropy Weight Method (EWM) was applied to combine network analysis and machine learning outcomes for a comprehensive risk evaluation. The study identified key risk factors for SIB, including loneliness, ADHD symptoms, Internet addiction, anxiety, depression, affinity for solitude, autistic traits, being bullied. These factors interact within a complex network structure, influencing the occurrence of SIB both directly and indirectly. The integration of EWM, network analysis, and machine learning provides a more precise risk assessment approach for adolescent SIB. The findings offer valuable insights into the causal mechanisms of SIB and emphasize the importance of targeted prevention and intervention strategies.

The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge in achieving stable and high-performance cooperative behavior. Addressing the need for the balanced exploration and exploitation of multi-agent ant robots within a partially observable continuous action space, this study introduces a multi-agent centralized strategy gradient algorithm grounded in a local state transition mechanism. In order to solve this challenge, the algorithm learns local state and local state-action representation from local observations and action values, thereby establishing a “local state transition” mechanism autonomously. As the input of the actor network, the automatically extracted local observation representation reduces the input state dimension, enhances the local state features closely related to the local state transition, and promotes the agent to use the local state features that affect the next observation state. To mitigate non-stationarity and reliability assignment issues in multi-agent environments, a centralized critic network evaluates the current joint strategy. The proposed algorithm, NST-FACMAC, is evaluated alongside other multi-agent deterministic strategy algorithms in a continuous control simulation environment using a multi-agent ant robot. The experimental results indicate accelerated convergence and higher average reward values in cooperative multi-agent ant simulation environments. Notably, in four simulated environments named Ant-v2 (2 × 4), Ant-v2 (2 × 4d), Ant-v2 (4 × 2), and Manyant (2 × 3), the algorithm demonstrates performance improvements of approximately 1.9%, 4.8%, 11.9%, and 36.1%, respectively, compared to the best baseline algorithm. These findings underscore the algorithm’s effectiveness in enhancing the stability of multi-agent ant robot control within dynamic environments.